Over the past two decades, Topological Data Analysis (TDA), raised from Algebraic Topology, has found its way in the real-world applications.
In [1]}, TDA is used to model disease progression by inferring temporal phenotypes.
More recently [2]} use TDA and machine learning techniques to investigate genome mutation of SARS-COV-2.
Some other examples in biology include analysis of
brain neural activities [3]}, [4]}, and cancer genomics [5]}, [6]}. In this section, we aim to provide a brief overview of the primary TDA concept, i.e., the persistent homology. We avoid the mathematical detail which is beyond the scope of this article. For a thorough description see [7]}, [8]}.
